What a start!

This is a little R investigation to the question:

How did the Lynx come back to win so decisively?

Let’s get started with some WNBA R packages.

library(wehoop)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(tidyr)
library(ggplot2)
library(ggforce)
library(gtExtras)
## Loading required package: gt
wnba_pbp <- wehoop::load_wnba_pbp()
finals_game_one_id <- 401726988

finals_game_one_pbp <- wnba_pbp %>%
  filter(game_id == finals_game_one_id)
#finals_game_one_pbp %>%
#  View()
# 
player_court_id <- 2987891
player_phee_id <- 3917450
# 
# finals_game_one_pbp %>%
#   filter(athlete_id_1 == player_court_id) %>%
#   filter(shooting_play == TRUE) %>%
#   View()
# 
# finals_game_one_pbp %>%
#   filter(athlete_id_1 == player_phee_id) %>%
#   filter(shooting_play == TRUE) %>%
#   View()
# 
finals_game_one_pbp %>%
  mutate(coordinate_x_raw = 50-coordinate_x_raw) %>%
  filter(athlete_id_1 == player_court_id) %>%
  filter(shooting_play == TRUE) %>%
  ggplot(aes(x = coordinate_x_raw, y = coordinate_y_raw, color = scoring_play, size = 8)) +
  geom_jitter() +
    geom_segment(aes(x = 0, y = -4, xend = 50, yend = -4, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 17, y = 13, xend = 33, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 17, y = -4, xend = 17, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 19, y = -4, xend = 19, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 31, y = -4, xend = 31, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 33, y = -4, xend = 33, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 22, y = -0.75, xend = 28, yend = -0.75, alpha =0.1), inherit.aes = FALSE) +
    geom_circle(aes(x0 = 25, y0 = 0, r = 0.75), inherit.aes = FALSE) +
    geom_arc(aes(x0 = 25, y0= 0, r = 22.145, start = -1.2, end = 1.2), inherit.aes = FALSE) +
    #geom_segment(aes(x = 3, y = -4, xend = 3, yend = 10, alpha =0.1), inherit.aes = FALSE) +
    #geom_segment(aes(x = 47, y = -4, xend = 47, yend = 10, alpha =0.1), inherit.aes = FALSE) +
    scale_size(guide = "none") +
    scale_alpha(guide = "none") +
    xlab("")+
    ylab("")+
    ggtitle("This chart was an excuse to watch Courtney Williams replays") +
    theme_void() +
    theme(plot.title = element_text(hjust = 0.5))

finals_game_one_pbp %>%
  mutate(coordinate_x_raw = 50-coordinate_x_raw) %>%
  filter(athlete_id_1 == player_phee_id) %>%
  filter(shooting_play == TRUE) %>%
  ggplot(aes(x = coordinate_x_raw, y = coordinate_y_raw, color = scoring_play, size = 8)) +
  geom_jitter() +
    geom_segment(aes(x = 0, y = -4, xend = 50, yend = -4, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 17, y = 13, xend = 33, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 17, y = -4, xend = 17, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 19, y = -4, xend = 19, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 31, y = -4, xend = 31, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 33, y = -4, xend = 33, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 22, y = -0.75, xend = 28, yend = -0.75, alpha =0.1), inherit.aes = FALSE) +
    geom_circle(aes(x0 = 25, y0 = 0, r = 0.75), inherit.aes = FALSE) +
    geom_arc(aes(x0 = 25, y0= 0, r = 22.145, start = -1.2, end = 1.2), inherit.aes = FALSE) +
    #geom_segment(aes(x = 3, y = -4, xend = 3, yend = 10, alpha =0.1), inherit.aes = FALSE) +
    #geom_segment(aes(x = 47, y = -4, xend = 47, yend = 10, alpha =0.1), inherit.aes = FALSE) +
    scale_size(guide = "none") +
    scale_alpha(guide = "none") +
    xlab("")+
    ylab("")+
    ggtitle("Phee is incredible") +
    theme_void() +
    theme(plot.title = element_text(hjust = 0.5))

# find all 4 point_plays

wnba_pbp %>%
  mutate(day = as.Date(wallclock)) %>%
  group_by(home_team_mascot, away_team_mascot, season, game_id, day, athlete_id_1,period_display_value, clock_display_value) %>%
  summarize(total_points = sum(score_value)) %>%
  filter(total_points > 3) %>%
  arrange(desc(total_points))
## `summarise()` has grouped output by 'home_team_mascot', 'away_team_mascot',
## 'season', 'game_id', 'day', 'athlete_id_1', 'period_display_value'. You can
## override using the `.groups` argument.
## # A tibble: 20 × 9
## # Groups:   home_team_mascot, away_team_mascot, season, game_id, day,
## #   athlete_id_1, period_display_value [20]
##    home_team_mascot away_team_mascot season   game_id day        athlete_id_1
##    <chr>            <chr>             <int>     <int> <date>            <int>
##  1 Wings            Fever              2024 401620409 2024-09-01      3142191
##  2 Aces             Lynx               2024 401620285 2024-06-12      2529205
##  3 Dream            Aces               2024 401620257 2024-06-01      4065870
##  4 Fever            Mercury            2024 401620370 2024-08-16      4433403
##  5 Fever            Sun                2024 401620232 2024-05-20      3142191
##  6 Fever            Sun                2024 401620397 2024-08-29          869
##  7 Liberty          Lynx               2024 401726988 2024-10-11      2987891
##  8 Liberty          Sky                2024 401620239 2024-05-24      2998928
##  9 Liberty          Sun                2024 401620362 2024-07-16      4066533
## 10 Lynx             Sparks             2024 401620455 2024-09-20      2987891
## 11 Lynx             Storm              2024 401620226 2024-05-18      2529205
## 12 Lynx             Sun                2024 401725079 2024-10-09      2987891
## 13 Mercury          Sun                2024 401620440 2024-09-14      4068885
## 14 Mystics          Liberty            2024 401620448 2024-09-18      3056730
## 15 Sky              Fever              2024 401620313 2024-06-23      3142191
## 16 Sparks           Lynx               2024 401620270 2024-06-06      4398764
## 17 Storm            Mercury            2024 401620423 2024-09-08      2491205
## 18 Sun              Lynx               2024 401725077 2024-10-05      2529205
## 19 Wings            Aces               2024 401620396 2024-08-28      4281929
## 20 Wings            Sparks             2024 401620392 2024-08-25      4433630
## # ℹ 3 more variables: period_display_value <chr>, clock_display_value <chr>,
## #   total_points <int>

There was actually one FIVE-point play - kelsey mitchell was fouled by arike shooting a 3 on Sept 1st. Arike got a technical for complaining - so it was a 5-point play!

wnba_pbp %>%
  mutate(day = as.Date(wallclock)) %>%
  group_by(home_team_mascot, away_team_mascot, season, game_id, day, athlete_id_1,period_display_value, clock_display_value) %>%
  summarize(total_points = sum(score_value)) %>%
  filter(total_points > 3) %>%
  arrange(desc(total_points)) %>%
  select(total_points) %>%
  group_by(total_points) %>%
  summarise(plays_in_2024 = n()) %>%
  rename(total_points_on_play = total_points) %>%
  select(plays_in_2024, total_points_on_play) %>%
  gt() %>% 
  gt_theme_guardian() %>% 
  tab_header(title = "Four point games in 2024... wait, what?")
## `summarise()` has grouped output by 'home_team_mascot', 'away_team_mascot',
## 'season', 'game_id', 'day', 'athlete_id_1', 'period_display_value'. You can
## override using the `.groups` argument.
## Adding missing grouping variables: `home_team_mascot`, `away_team_mascot`,
## `season`, `game_id`, `day`, `athlete_id_1`, `period_display_value`
Four point games in 2024... wait, what?
plays_in_2024 total_points_on_play
19 4
1 5
 
finals_game_one_pbp %>%
  mutate(coordinate_x_raw = 50-coordinate_x_raw) %>%
  filter(athlete_id_1 == player_court_id) %>%
  filter(shooting_play == TRUE) %>%
  filter(sequence_number == 482) %>%
  ggplot(aes(x = coordinate_x_raw, y = coordinate_y_raw,  size = 8)) +
  geom_point() +
    geom_segment(aes(x = 0, y = -4, xend = 50, yend = -4, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 17, y = 13, xend = 33, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 17, y = -4, xend = 17, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 19, y = -4, xend = 19, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 31, y = -4, xend = 31, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 33, y = -4, xend = 33, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 22, y = -0.75, xend = 28, yend = -0.75, alpha =0.1), inherit.aes = FALSE) +
    geom_circle(aes(x0 = 25, y0 = 0, r = 0.75), inherit.aes = FALSE) +
    geom_arc(aes(x0 = 25, y0= 0, r = 22.145, start = -1.2, end = 1.2), inherit.aes = FALSE) +
    #geom_segment(aes(x = 3, y = -4, xend = 3, yend = 10, alpha =0.1), inherit.aes = FALSE) +
    #geom_segment(aes(x = 47, y = -4, xend = 47, yend = 10, alpha =0.1), inherit.aes = FALSE) +
    scale_size(guide = "none") +
    scale_alpha(guide = "none") +
    xlab("")+
    ylab("")+
    ggtitle("Wasn't this shot from the edge of the key?") +
    theme_void() +
    theme(plot.title = element_text(hjust = 0.5))

Now here’s Phee in the post-season

wnba_pbp %>%
  mutate(coordinate_x_raw = 50-coordinate_x_raw) %>%
  mutate(day = as.Date(wallclock)) %>%
  filter(day >= "2024-09-22") %>%
  filter(athlete_id_1 == player_court_id) %>%
  filter(shooting_play == TRUE) %>%
  filter(!grepl("Free Throw", type_text)) %>%
  filter(grepl("Jump Shot", type_text)) %>%
  filter(scoring_play == TRUE) %>%
  ggplot(aes(x = coordinate_x_raw, y = coordinate_y_raw, color = type_text, size = 8, alpha = 0.4)) +
  geom_jitter() +
    geom_segment(aes(x = 0, y = -4, xend = 50, yend = -4, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 17, y = 13, xend = 33, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 17, y = -4, xend = 17, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 19, y = -4, xend = 19, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 31, y = -4, xend = 31, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 33, y = -4, xend = 33, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 22, y = -0.75, xend = 28, yend = -0.75, alpha =0.1), inherit.aes = FALSE) +
    geom_circle(aes(x0 = 25, y0 = 0, r = 0.75), inherit.aes = FALSE) +
    geom_arc(aes(x0 = 25, y0= 0, r = 22.145, start = -1.2, end = 1.2), inherit.aes = FALSE) +
    #geom_segment(aes(x = 3, y = -4, xend = 3, yend = 10, alpha =0.1), inherit.aes = FALSE) +
    #geom_segment(aes(x = 47, y = -4, xend = 47, yend = 10, alpha =0.1), inherit.aes = FALSE) +
    scale_size(guide = "none") +
    scale_alpha(guide = "none") +
    xlab("")+
    ylab("")+
    ggtitle(" Courtney Williams is KILLING IT with high-momentum jump shots near the free-throw line") +
    theme_void() +
   # theme(plot.title = element_text(hjust = 0.5)) +
    theme(legend.text=element_text(size=6))

wnba_pbp %>%
  mutate(coordinate_x_raw = 50-coordinate_x_raw) %>%
  mutate(day = as.Date(wallclock)) %>%
  filter(day >= "2024-09-22") %>%
  filter(athlete_id_1 == player_court_id) %>%
  filter(shooting_play == TRUE) %>%
  filter(!grepl("Free Throw", type_text)) %>%
  filter(grepl("Jump Shot", type_text)) %>%
  filter(scoring_play == FALSE) %>%
  ggplot(aes(x = coordinate_x_raw, y = coordinate_y_raw, color = type_text, size = 8, alpha = 0.4)) +
  geom_jitter() +
    geom_segment(aes(x = 0, y = -4, xend = 50, yend = -4, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 17, y = 13, xend = 33, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 17, y = -4, xend = 17, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 19, y = -4, xend = 19, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 31, y = -4, xend = 31, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 33, y = -4, xend = 33, yend = 13, alpha =0.1), inherit.aes = FALSE) +
    geom_segment(aes(x = 22, y = -0.75, xend = 28, yend = -0.75, alpha =0.1), inherit.aes = FALSE) +
    geom_circle(aes(x0 = 25, y0 = 0, r = 0.75), inherit.aes = FALSE) +
    geom_arc(aes(x0 = 25, y0= 0, r = 22.145, start = -1.2, end = 1.2), inherit.aes = FALSE) +
    #geom_segment(aes(x = 3, y = -4, xend = 3, yend = 10, alpha =0.1), inherit.aes = FALSE) +
    #geom_segment(aes(x = 47, y = -4, xend = 47, yend = 10, alpha =0.1), inherit.aes = FALSE) +
    scale_size(guide = "none") +
    scale_alpha(guide = "none") +
    xlab("")+
    ylab("")+
    ggtitle(" Her traditional KILL ZONE is less successful this post-season. Here are her misses.") +
    theme_void() +
   # theme(plot.title = element_text(hjust = 0.5)) +
    theme(legend.text=element_text(size=6))

wnba_pbp %>%
  mutate(coordinate_x_raw = 50-coordinate_x_raw) %>%
  mutate(day = as.Date(wallclock)) %>%
  filter(day >= "2024-09-22") %>%
  filter(athlete_id_1 == player_court_id) %>%
  filter(shooting_play == TRUE) %>%
  filter(!grepl("Free Throw", type_text)) %>%
  group_by(type_text, scoring_play ) %>%
  summarize(count = n()) %>%
  pivot_wider(names_from = scoring_play , values_from = count, values_fill = 0) %>%
  mutate(attempts = `FALSE` + `TRUE`) %>%
  mutate(success_rate = `TRUE` / attempts) %>%
  rename(makes = `TRUE`) %>%
  arrange(desc(success_rate)) %>%
  select(-`FALSE`) %>%
  filter(grepl("Jump Shot", type_text)) %>%
  mutate(makes_and_attempts = paste0(makes, " for ", attempts)) %>%
  rename(jumpshot_type = type_text) %>%
  select(jumpshot_type, makes_and_attempts, success_rate) %>%
  gt() %>% 
  gt_theme_guardian() %>% 
  tab_header(title = "Since the start of this postseason, Courtney has struggled with pullup jump shots - except when she's mid-sprint!!")
## `summarise()` has grouped output by 'type_text'. You can override using the
## `.groups` argument.
Since the start of this postseason, Courtney has struggled with pullup jump shots - except when she's mid-sprint!!
makes_and_attempts success_rate
Driving Floating Bank Jump Shot
1 for 1 1.0000000
Fade Away Jump Shot
1 for 1 1.0000000
Running Pullup Jump Shot
5 for 8 0.6250000
Jump Shot
13 for 21 0.6190476
Pullup Jump Shot
14 for 46 0.3043478
Driving Floating Jump Shot
0 for 1 0.0000000
Running Jump Shot
0 for 2 0.0000000

Let’s compare with Phee

wnba_pbp %>%
  mutate(coordinate_x_raw = 50-coordinate_x_raw) %>%
  mutate(day = as.Date(wallclock)) %>%
  filter(day >= "2024-09-22") %>%
  filter(athlete_id_1 == player_phee_id) %>%
  filter(shooting_play == TRUE) %>%
  filter(!grepl("Free Throw", type_text)) %>%
  group_by(type_text, scoring_play ) %>%
  summarize(count = n()) %>%
  pivot_wider(names_from = scoring_play , values_from = count, values_fill = 0) %>%
  mutate(attempts = `FALSE` + `TRUE`) %>%
  mutate(success_rate = `TRUE` / attempts) %>%
  rename(makes = `TRUE`) %>%
  arrange(desc(attempts)) %>%
  select(-`FALSE`) %>%
  filter(grepl("Jump Shot", type_text)) %>%
  mutate(makes_and_attempts = paste0(makes, " for ", attempts)) %>%
  rename(jumpshot_type = type_text) %>%
  select(jumpshot_type, makes_and_attempts, success_rate) %>%
  gt() %>% 
  gt_theme_guardian() %>% 
  tab_header(title = "Napheesa Collier shows a similar inclination towards more complex jump shots. But hers are pivot-y!")
## `summarise()` has grouped output by 'type_text'. You can override using the
## `.groups` argument.
Napheesa Collier shows a similar inclination towards more complex jump shots. But hers are pivot-y!
makes_and_attempts success_rate
Jump Shot
10 for 32 0.3125000
Turnaround Jump Shot
10 for 16 0.6250000
Turnaround Fade Away Jump Shot
5 for 11 0.4545455
Fade Away Jump Shot
4 for 7 0.5714286
Pullup Jump Shot
2 for 5 0.4000000
Turnaround Bank Jump Shot
2 for 2 1.0000000
Driving Floating Bank Jump Shot
1 for 1 1.0000000
Driving Floating Jump Shot
0 for 1 0.0000000
Floating Jump Shot
0 for 1 0.0000000
Jump Shot Bank
0 for 1 0.0000000
Running Jump Shot
1 for 1 1.0000000
Step Back Jump Shot
1 for 1 1.0000000

Including Plots

You can also embed plots, for example:

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.